Title page for ETD etd-09252009-115846


Type of Document Master's Thesis
Author Errasquin, Leonardo
Author's Email Address errasqui@vt.edu
URN etd-09252009-115846
Title Airfoil Self-Noise Prediction Using Neural Networks for Wind Turbines
Degree Master of Science
Department Mechanical Engineering
Advisory Committee
Advisor Name Title
Burdisso, Ricardo A. Committee Chair
Devenport, William J. Committee Member
Johnson, Martin E. Committee Member
Keywords
  • Noise
  • wind turbine
  • neural network
  • airfoil
Date of Defense 2009-09-10
Availability unrestricted
Abstract
A neural network prediction method has been developed to compute self-noise of airfoils typically used in wind turbines. The neural networks were trained using experimental data corresponding to tests of several different airfoils over a range of flow conditions. The experimental data corresponds to the NACA 0012, Delft DU96, Sandia S831, S822 and S834, Fx63-137, SG6043 and SD-2030 airfoils. The chord of these airfoils range from 0.025 to 0.91 m and they were tested at Reynolds numbers of up to 3.8 million and angle of attack up to 15o depending on the airfoil. Using experimental data corresponding to different airfoils provides to the neural network the capacity to take into account the geometry of the airfoils in the predictions.geometry of the airfoils in the predictions. The input parameters to the network are the flow speed, chord length, effective angle of attack and parameters describing the geometrical shape of the airfoil. In addition, boundary layer displacement thickness was used for some models. The parameters used for taking into account the airfoil’s geometry are based on a conformal mapping method or a polynomial approximation. The output of the neural network is given by sound pressure level in 1/3rd octave bands for nine frequencies ranging from 630 to 4000 Hz.

The present work constitutes an application of neural networks to aeroacoustics. The

main objective was to assess the potential of using neural networks to model airfoil noise.

Therefore, this work is focused in the modeling of the problem, and no mathematical analyses

about neural networks are intended. To this end, several models were investigated both in terms

of the configuration and training approach. The performance of the networks was evaluated for a

range of flow conditions. The neural network technique was first investigated for the NACA

0012 airfoil only. For this case, the geometry of the airfoil was not incorporated as input into the

model. The neural network approach was then extended to account for airfoils of any geometry

by including data from all airfoils in the training.

Airfoil Self-Noise Prediction Using Neural Networks for Wind Turbines Leonardo Errasquin

The results show that the neural networks are capable of predicting the airfoils self-noise

reasonably well for most of the flow conditions. The broadband noise due to the turbulent

boundary layer interacting with the trailing edge is estimated very well. The tonal vortex

shedding noise due to laminar boundary layer-trailing edge interaction is not predicted as well,

most likely due to the limited data available for this noise source. In summary, the research here

demonstrated the potential of the neural network as a tool to predict noise from typical wind

turbine airfoils.

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